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Title: Intelligent assembly of wind turbine hubs
Author: Deters, Christian
ISNI:       0000 0004 5350 0332
Awarding Body: King's College London (University of London)
Current Institution: King's College London (University of London)
Date of Award: 2014
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The fast growing wind turbine industry is expected to play a major role in solving our energy needs in the future. At present turbine manufacturing is performed mostly manually. Due to market growth the economical peak point is reached where an automated assembly concept can be introduced. This thesis focuses on the assembly of the wind turbine hub, in particular, on the bolt tightening process for the wind turbine bearing assembly, contributing to EU project COSMOS. Within this industrial research project, bolt tightening has been identified as an important research problem and the control strategies derived in this PhD thesis contributed to the activities of COSMOS. A wind turbine hub has three bearings which are assembled using multiple bolts (in current wind turbines, this can be up to 128 bolts). With the need to conform to stringent safety requirements and with the aim to produce long-lasting systems, the desired clamping force between the nut and a counteracting flange needs to be accurately and reliably achieved as a result of the tightening process. This thesis analyses the bolt tightening process divided into several tightening stages, with each stage addressing particular control and safety problems. The introduced fuzzy control architecture makes use of membership functions combined with linguistic rules to set the control target (which are specific torque and angle levels for the investigated wind turbine assembly process) to ensure that the desired clamping force is reached successfully and accurately. The control results (step response of the final control values and final clamping force) have been compared to more traditional control paradigms, including the proportional-integral-derivative (PID) controller. Experiments have shown that the accuracy improved and the standard deviation of the Fuzzy controller is more than 4 times lower than the one achieved using the PID controller. The bolt system has been further analysed and a numerical state space model has been identified using an experimental identification method. The found model has been used to identify suitable control gains for a proportional-integral (PI) control strategy and were then fine-tuned using an online learning process based on a genetic algorithm (GA). Error detection and avoidance is another important aspect when assembling safety-critical systems such as wind turbines. This PhD study introduces an error detection mechanism that is active during the bolt tightening process and integrated with the fuzzy control architecture used for bolt tightening. This is achieved by defining additional membership functions and linguistic rules for error detection. The error detection mechanism is based on a logic based approach terminating the tightening process when critical control parameters are exceeded.
Supervisor: Lam, Hak-Keung; Althoefer, Kaspar Alexander Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available